KNIME focuses on building, training, and evaluating machine learning models using a visual, no-code/low-code workflow environment. It enables users to perform data preprocessing, feature engineering, model selection, and performance evaluation within integrated pipelines. This training explains how to apply supervised and unsupervised learning techniques such as classification, regression, clustering, and dimensionality reduction. It also covers model validation, hyperparameter tuning, and deployment strategies for real-world applications. You will learn how organizations use KNIME to automate machine learning workflows and improve decision-making. The course also highlights best practices for building scalable and efficient ML solutions in enterprise environments.
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